# NHTSA Vehicle Recalls & Complaints Scraper (`scrapers_lat/nhtsa-recalls-scraper`) Actor
Scrape official US vehicle safety recalls and consumer complaints from the NHTSA database by make, model and year. Get recall campaign numbers, affected components, defect summaries, consequences, remedies and injury reports. Export to JSON, CSV or Excel.
- **URL**: https://apify.com/scrapers\_lat/nhtsa-recalls-scraper.md
- **Developed by:** [Scrapers Lat](https://apify.com/scrapers_lat) (community)
- **Categories:** Business, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $2.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
[](https://console.apify.com/actors/gNAcOpoAQ6MTnhTkv/input)
## NHTSA Vehicle Recalls & Complaints Scraper
> Extract official safety recalls and consumer complaints from the US NHTSA database for any vehicle make, model and model year
2 datasets recalls and complaints
United States coverage
JSON / CSV / Excel output formats
### What you get
Each record is one safety recall campaign or one consumer complaint pulled straight from the National Highway Traffic Safety Administration, the US federal agency that tracks vehicle defects. Pick the recalls dataset for official manufacturer recall campaigns, or the complaints dataset for problems drivers reported to NHTSA. Provide any combination of makes, models and model years and the actor queries each combination and returns the matching records.
Recall fields:
- **dataset**: recalls for this record
- **campaignNumber**: the NHTSA recall campaign number, for example 20V771000
- **make**: vehicle make, for example HONDA
- **model**: vehicle model, for example ACCORD
- **modelYear**: vehicle model year
- **component**: the affected vehicle system, for example ELECTRICAL SYSTEM:BODY CONTROL MODULE:SOFTWARE
- **summary**: full description of the defect and which vehicles are involved
- **consequence**: what can go wrong and the safety risk it creates
- **remedy**: how the manufacturer will fix the issue and the owner contact details
- **notes**: additional notes, often the NHTSA safety hotline
- **manufacturer**: the manufacturer that filed the recall
- **reportReceivedDate**: date NHTSA received the recall report
- **parkIt**: true when owners are told to stop driving the vehicle immediately
- **parkOutSide**: true when owners are told to park away from buildings due to fire risk
- **overTheAirUpdate**: true when the fix is delivered as an over-the-air software update
Complaint fields:
- **dataset**: complaints for this record
- **odiNumber**: the NHTSA Office of Defects Investigation complaint number
- **make**: vehicle make
- **model**: vehicle model
- **modelYear**: vehicle model year
- **component**: the system the complaint is about, for example STRUCTURE
- **summary**: the consumer's own description of the problem
- **manufacturer**: the manufacturer named in the complaint
- **crash**: true when the complaint involved a crash
- **fire**: true when the complaint involved a fire
- **numberOfInjuries**: number of injuries reported
- **numberOfDeaths**: number of deaths reported
- **dateOfIncident**: date the incident happened
- **dateComplaintFiled**: date the complaint was filed with NHTSA
- **vin**: the partial VIN the consumer supplied, when provided
- **products**: array of every vehicle or product named on the complaint, each with type, make, model, modelYear and manufacturer (multi-vehicle complaints list all of them, not just the first)
- **observedAt**: when this record was collected by the scraper
### Who is it for
| Use case | Who benefits |
|---|---|
| Monitor recalls across a fleet by make, model and year | Fleet managers and rental companies |
| Surface open safety recalls before listing a used vehicle | Dealers and used-car marketplaces |
| Track complaint trends and defect signals for specific models | Automotive analysts and journalists |
| Feed recall and complaint data into a consumer safety app | Auto-tech and insurance product teams |
| Research defect patterns for litigation or due diligence | Legal and research teams |
### Frequently Asked Questions
**Does this cover every car brand sold in the United States?**
Yes. NHTSA tracks recalls and complaints for vehicles sold in the US market, so any make and model with records in the federal database can be queried. Spell the make and model the way NHTSA does, for example Honda and Accord, and provide the model year.
**What is the difference between the recalls and complaints datasets?**
Recalls are official campaigns filed by manufacturers when a defect needs a fix, each with a campaign number, consequence and remedy. Complaints are problems individual drivers reported to NHTSA, each with the consumer's own description. Choose which one to pull with the Dataset option.
**Can I pull several models or years at once?**
Yes. Add multiple makes, models and model years and the actor runs every combination. For example two makes, one model and two years runs four queries and combines all matching records into one dataset.
**Why are make, model and year all required?**
The NHTSA recall and complaint endpoints return data per exact vehicle, so each query needs all three. If a combination has no records the actor simply moves on and reports nothing for it rather than failing.
**What happens when a field has no value?**
Fields that NHTSA does not provide for a record are returned as null and never invented. Recall-only fields are null on complaint records and complaint-only fields are null on recall records.
### Example use cases
Ready-to-run example tasks, each preconfigured for a common scenario. Open one and press run, or use it as a template:
- [Ford Explorer 2020 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/ford-explorer-2020-safety-recalls): Get official NHTSA Ford Explorer 2020 safety recall campaigns with component, summary, consequence and remedy details.
- [Ford F-150 2019 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/ford-f150-2019-safety-recalls): Get official NHTSA Ford F-150 2019 safety recall campaigns with component, summary, consequence and remedy details.
- [Honda Accord 2018 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/honda-accord-2018-safety-recalls): Get official NHTSA Honda Accord 2018 safety recall campaigns with component, summary, consequence and remedy details.
- [Honda Civic 2016 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/honda-civic-2016-safety-recalls): Get official NHTSA Honda Civic 2016 safety recall campaigns with component, summary, consequence and remedy details.
- [Jeep Wrangler 2018 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/jeep-wrangler-2018-safety-recalls): Get official NHTSA Jeep Wrangler 2018 safety recall campaigns with component, summary, consequence and remedy details.
- [Nissan Altima 2019 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/nissan-altima-2019-safety-recalls): Get official NHTSA Nissan Altima 2019 safety recall campaigns with component, summary, consequence and remedy details.
- [Ram 1500 2019 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/ram-1500-2019-safety-recalls): Get official NHTSA Ram 1500 2019 safety recall campaigns with component, summary, consequence and remedy details.
- [Subaru Outback 2020 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/subaru-outback-2020-safety-recalls): Get official NHTSA Subaru Outback 2020 safety recall campaigns with component, summary, consequence and remedy details.
- [Tesla Model 3 2021 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/tesla-model-3-2021-safety-recalls): Get official NHTSA Tesla Model 3 2021 safety recall campaigns with component, summary, consequence and remedy details.
- [Toyota Camry 2020 Safety Recalls](https://apify.com/scrapers_lat/nhtsa-recalls-scraper/examples/toyota-camry-2020-safety-recalls): Get official NHTSA Toyota Camry 2020 safety recall campaigns with component, summary, consequence and remedy details.
### Export, API and AI agents (x402 + MCP)
Export the scraped data to **JSON, CSV or Excel**, pull it as a **dataset** through the Apify **API**, or wire it into your app with **no code**. This web scraper and data extractor also works for bulk data extraction and scheduled runs.
For AI agents: this Actor is available on **x402**, Apify's agentic payment standard built with Coinbase. An AI agent can discover, pay for and run it on its own with a funded wallet and a single HTTP request: no account, no subscription, no API key and no human in the loop. It also runs as an **MCP** tool inside Claude, Cursor and other AI clients out of the box. Learn more about [x402 agentic payments on Apify](https://docs.apify.com/platform/integrations/x402).
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Need data from the same space? Here are other scrapers we build and maintain:
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- [openFDA Drug Events & Recalls Scraper](https://apify.com/scrapers_lat/openfda-drug-events-scraper): Scrape FDA drug adverse event reports (FAERS) and drug recall and enforcement actions from the...
- [Grants.gov Opportunity Scraper](https://apify.com/scrapers_lat/grants-gov-scraper): Extract U.S. federal grant opportunities from the official Grants.gov API. Scrape title, agency...
- [NPPES NPI Provider Scraper](https://apify.com/scrapers_lat/nppes-npi-scraper): Scrape US healthcare provider records from the official NPPES NPI Registry. Search by name...
- [BizQuest Businesses-for-Sale Listings Scraper](https://apify.com/scrapers_lat/bizquest-scraper): Extract US businesses-for-sale listings from BizQuest with the broker or seller contact (name...
### More scrapers at scrapers.lat
This actor is built and maintained by [scrapers.lat](https://scrapers.lat), where we publish scrapers for Latin American and US public platforms: real estate, jobs, e-commerce, company registries and government data. Browse the full catalog, see live sample output for each one, or ask us for a custom scraper at [scrapers.lat](https://scrapers.lat).
---
> This actor is an independent tool and has no affiliation with the National Highway Traffic Safety Administration or the US Department of Transportation. It accesses only the recall and complaint data that NHTSA publishes as a public record. Use this data lawfully and responsibly and respect the NHTSA terms of use.
# Actor input Schema
## `maxItems` (type: `integer`):
Maximum number of recall or complaint records to collect across all make, model and year combinations. Optional.
## `dataset` (type: `string`):
Choose what to collect: official manufacturer safety recall campaigns, or consumer complaints filed with NHTSA. Each combination of make, model and year is queried for the selected dataset.
## `makes` (type: `array`):
Vehicle makes to query, for example Honda, Toyota, Ford. One make per line.
## `models` (type: `array`):
Vehicle models to query, for example Accord, Camry, F-150. One model per line.
## `modelYears` (type: `array`):
Model years to query, for example 2020, 2021. One four-digit year per line.
## Actor input object example
```json
{
"maxItems": 100,
"dataset": "recalls",
"makes": [
"Honda"
],
"models": [
"Accord"
],
"modelYears": [
"2020"
]
}
````
# Actor output Schema
## `results` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"maxItems": 100,
"makes": [
"Honda"
],
"models": [
"Accord"
],
"modelYears": [
"2020"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapers_lat/nhtsa-recalls-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"maxItems": 100,
"makes": ["Honda"],
"models": ["Accord"],
"modelYears": ["2020"],
}
# Run the Actor and wait for it to finish
run = client.actor("scrapers_lat/nhtsa-recalls-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"maxItems": 100,
"makes": [
"Honda"
],
"models": [
"Accord"
],
"modelYears": [
"2020"
]
}' |
apify call scrapers_lat/nhtsa-recalls-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapers_lat/nhtsa-recalls-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "NHTSA Vehicle Recalls & Complaints Scraper",
"description": "Scrape official US vehicle safety recalls and consumer complaints from the NHTSA database by make, model and year. Get recall campaign numbers, affected components, defect summaries, consequences, remedies and injury reports. Export to JSON, CSV or Excel.",
"version": "0.1",
"x-build-id": "NiSY530arBiup9vIG"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapers_lat~nhtsa-recalls-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapers_lat-nhtsa-recalls-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapers_lat~nhtsa-recalls-scraper/runs": {
"post": {
"operationId": "runs-sync-scrapers_lat-nhtsa-recalls-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapers_lat~nhtsa-recalls-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrapers_lat-nhtsa-recalls-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"makes",
"models",
"modelYears"
],
"properties": {
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 1000000,
"type": "integer",
"description": "Maximum number of recall or complaint records to collect across all make, model and year combinations. Optional."
},
"dataset": {
"title": "Dataset",
"enum": [
"recalls",
"complaints"
],
"type": "string",
"description": "Choose what to collect: official manufacturer safety recall campaigns, or consumer complaints filed with NHTSA. Each combination of make, model and year is queried for the selected dataset.",
"default": "recalls"
},
"makes": {
"title": "Makes",
"type": "array",
"description": "Vehicle makes to query, for example Honda, Toyota, Ford. One make per line.",
"items": {
"type": "string"
}
},
"models": {
"title": "Models",
"type": "array",
"description": "Vehicle models to query, for example Accord, Camry, F-150. One model per line.",
"items": {
"type": "string"
}
},
"modelYears": {
"title": "Model Years",
"type": "array",
"description": "Model years to query, for example 2020, 2021. One four-digit year per line.",
"items": {
"type": "string"
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```